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> ML_LITERATURE // CHEN-2018-TVM-AUTOMATED-END-TO-END-OPTIMIZING-COMPILER-FOR-DEEP-LEARNING_v1.0

TVM: An Automated End-to-End Optimizing Compiler for Deep Learning

Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, Arvind Krishnamurthy · USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2018)

hardware-compiler2018industry-standardartifactsAvailable
arXiv:1802.04799Back to Literature

Principal Contribution

Automated deep learning optimizing compiler using machine learning to guide tensor code generation across diverse CPUs, GPUs, and custom accelerators.

Operational Relevance

Directly guides deployment choices and architecture selection for task-model-compilation, task-edge-mobile-inference.

Assumptions

  • Standard empirical regularity and statistical stability hold across evaluation domains

Limitations

  • Performance characteristics depend on domain distribution and compute allocation parameters

Connected Algorithms, Architectures & Tools

Related Algorithms:
Related Architectures:
Implementing Libraries: